# Raiders of the Lost Art

arXiv: https://arxiv.org/abs/1909.05677 · HTML: https://mlsanity.com/paper/1909.05677/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars](https://mlsanity.com/paper/2101.10807/) (2021)
- [Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks](https://mlsanity.com/paper/2102.00209/) (2021)
- [Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion](https://mlsanity.com/paper/2111.12602/) (2021)

## Abstract (arXiv)

Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.